Business Intelligence

1. 1The 6 benefits of Bi are: [blank_start]Fast answers to any business query[blank_end], [blank_start]enables real-time analysis[blank_end] with quick navigation, [blank_start]identify cross-selling[blank_end] and up-selling opportunities, reduces [blank_start]the risk of bottlenecks[blank_end], helps to know [blank_start]the business better[blank_end], improves [blank_start]decision making[blank_end].

   1. AFast answers to any business query
   2. Benables real-time analysis
   3. Cidentify cross-selling
   4. Dthe risk of bottlenecks
   5. Ethe business better
   6. Fdecision making
2. 2Bi combines 3 disciplines, these are: [blank_start]statistics, technology and business[blank_end]

   1. Astatistics, technology and business
3. 3The 4 types of data are: [blank_start]Internal, external, structured[blank_end] and [blank_start]unstructured.[blank_end]

   1. AInternal, external, structured
   2. Bunstructured.
4. 4What are the 10 steps of the Bi process? [blank_start]Priority[blank_end], [blank_start]questions[blank_end], [blank_start]information[blank_end], [blank_start]data[blank_end], [blank_start]visualisation[blank_end], [blank_start]data quality[blank_end], [blank_start]ethics[blank_end], [blank_start]metrics and performance indicators[blank_end], [blank_start]insights[blank_end], [blank_start]more questions[blank_end].

   1. APriority
   2. Bquestions
   3. Cinformation
   4. Ddata
   5. Evisualisation
   6. Fdata quality
   7. Gethics
   8. Hmetrics and performance indicators
   9. Iinsights
   10. Jmore questions
5. 5What is a good priority? It [blank_start]distinguishes the organisation[blank_end] and is [blank_start]something the CEO would be interested in[blank_end] and [blank_start]would affect the whole organisation[blank_end].

   1. Adistinguishes the organisation
   2. Bsomething the CEO would be interested in
   3. Cwould affect the whole organisation
6. 6What are the three decision levels? [blank_start]Strategic[blank_end], [blank_start]tactical[blank_end] and [blank_start]operational[blank_end].

   1. AStrategic
   2. Btactical
   3. Coperational
7. 7The attributes of strategic level decisions are [blank_start]long term[blank_end] and [blank_start]complex[blank_end] [blank_start]e.g. aim to be market leader[blank_end]. The attributes of tactical level decisions are [blank_start]medium term[blank_end] and [blank_start]less complex[blank_end] [blank_start]e.g. launching a new product[blank_end]. The attributes of operational level decisions are [blank_start]day-to day[blank_end], [blank_start]simple and routine[blank_end] [blank_start]e.g. regular ordering of supplies[blank_end].

   1. Along term
   2. Bcomplex
   3. Ce.g. aim to be market leader
   4. Dmedium term
   5. Eless complex
   6. Fe.g. launching a new product
   7. Gday-to day
   8. Hsimple and routine
   9. Ie.g. regular ordering of supplies
8. 8What level of management are these decisions? Strategic = [blank_start]senior[blank_end], Tactical = [blank_start]middle[blank_end] and operational = [blank_start]junior[blank_end]

   1. Asenior
   2. Bmiddle
   3. Cjunior
9. 9What are the 3 types of analytics? [blank_start]Descriptive[blank_end], [blank_start]Predictive[blank_end] and [blank_start]prescriptive[blank_end].

   1. ADescriptive
   2. BPredictive
   3. Cprescriptive
10. 10What do descriptive analytics tell us? [blank_start]What happened[blank_end], [blank_start]what[blank_end] is [blank_start]happening[blank_end], leads to [blank_start]well-defined business problems[blank_end] and [blank_start]opportunities[blank_end].

    1. AWhat happened
    2. Bwhat
    3. Chappening
    4. Dwell-defined business problems
    5. Eopportunities

11. 11What do predictive analytics tell us? What [blank_start]will happen[blank_end], [blank_start]why it will[blank_end] happen resulting in [blank_start]accurate projections of future events[blank_end] and [blank_start]outcomes[blank_end].

    1. Awill happen
    2. Bwhy it will
    3. Caccurate projections of future events
    4. Doutcomes
12. 12What do prescriptive analytics tell us? What [blank_start]should I do[blank_end], why [blank_start]should I do it[blank_end], resulting in [blank_start]best possible business decisions[blank_end] and [blank_start]actions[blank_end].

    1. Ashould I do
    2. Bshould I do it
    3. Cbest possible business decisions
    4. Dactions
13. 13With the growth of [blank_start]Big Data[blank_end], it is increasingly important that businesses can [blank_start]interpret large volumes of data[blank_end] that they [blank_start]collect and manage[blank_end]. [blank_start]Data visualisation[blank_end] converts data into a [blank_start]visual format[blank_end] e.g. [blank_start]graph, chart or diagram[blank_end] making it easier for businesses to detect [blank_start]patterns and trends[blank_end]. Good data visualisation also allows businesses to [blank_start]easily gain insights[blank_end] from [blank_start]complex datasets[blank_end] in order to make crucial informed [blank_start]decisions[blank_end].

    1. ABig Data
    2. Binterpret large volumes of data
    3. Ccollect and manage
    4. DData visualisation
    5. Evisual format
    6. Fgraph, chart or diagram
    7. Gpatterns and trends
    8. Heasily gain insights
    9. Icomplex datasets
    10. Jdecisions
14. 14What is big data? Big Data is a term used loosely to refer to [blank_start]large[blank_end], [blank_start]fast[blank_end] or [blank_start]complex[blank_end] data that is [blank_start]difficult[blank_end] or virtually impossible to manage using traditional database processing methods.

    1. Alarge
    2. Bfast
    3. Ccomplex
    4. Ddifficult
15. 15What are the 5 V's that make up big data? [blank_start]Volume[blank_end], [blank_start]Velocity[blank_end], [blank_start]Variety[blank_end], [blank_start]Veracity[blank_end] and [blank_start]Value[blank_end]

    1. AVolume
    2. BVelocity
    3. CVariety
    4. DVeracity
    5. EValue
16. 16What are the 3 visualisation techniques to use when creating dashboard? [blank_start]Pre-attentive attributes[blank_end], the [blank_start]rule of thirds[blank_end] and the [blank_start]golden ratio[blank_end].

    1. APre-attentive attributes
    2. Brule of thirds
    3. Cgolden ratio
17. 17Good visualisations…
    - [blank_start]show the data[blank_end]
    - [blank_start]do not get in the way of the message[blank_end]
    - [blank_start]avoid distortion[blank_end]
    - [blank_start]present many numbers in a small space[blank_end]
    - [blank_start]make large data sets coherent[blank_end]
    - [blank_start]encourage comparison between data[blank_end]
    - supply both a [blank_start]broad overview and fine detail[blank_end]
    - [blank_start]serve a clear purpose[blank_end]

    1. Ashow the data
    2. Bdo not get in the way of the message
    3. Cavoid distortion
    4. Dpresent many numbers in a small space
    5. Emake large data sets coherent
    6. Fencourage comparison between data
    7. Gbroad overview and fine detail
    8. Hserve a clear purpose
18. 18What is a dashboard? A collection of visualisations assembled to create a single unified [blank_start]visual display[blank_end].

    1. Avisual display
19. 19What is structured data? Comprised of clearly defined data fields e.g. [blank_start]names[blank_end], [blank_start]ages[blank_end], [blank_start]addresses[blank_end], phone numbers etc

    1. Anames
    2. Bages
    3. Caddresses
20. 20What is unstructured data? The data is usually not as easily [blank_start]searchable[blank_end], sources include: social media, mobile applications, [blank_start]location services[blank_end], sensors, emails, [blank_start]spreadsheets[blank_end], [blank_start]PDFs[blank_end], PowerPoint, Word docs, voice recordings and internet of things technologies. It is [blank_start]hard to manage[blank_end] and [blank_start]creativity[blank_end] is required to pull relevant data for analytics.
    

    1. Asearchable
    2. Blocation services
    3. Cspreadsheets
    4. DPDFs
    5. Ehard to manage
    6. Fcreativity
21. 21Data is transformed into information by adding value through:
    1. contextualising = we know in [blank_start]what context and for what purpose[blank_end] data were collected
    2. categorising = the units of [blank_start]analysis or key components[blank_end] of the data are known
    3. calculating = the data may have been processed [blank_start]mathematically or statistically[blank_end]
    4. quality (corrected) = the data may have had [blank_start]errors or inconsistencies eliminated[blank_end]
    5. condensing = [blank_start]summarising data more concisely[blank_end] e.g. aggregation

    1. Awhat context and for what purpose
    2. Banalysis or key components
    3. Cmathematically or statistically
    4. Derrors or inconsistencies eliminated
    5. Esummarising data more concisely
22. 22The three major roles of an organisation information system are:
    • Support [blank_start]business processes and operations[blank_end]
    • Support [blank_start]business decision making[blank_end]
    • Support [blank_start]competitive advantage[blank_end] and [blank_start]decision making[blank_end]

    1. Abusiness processes and operations
    2. Bbusiness decision making
    3. Ccompetitive advantage
    4. Ddecision making
23. 23Enterprise Information Management (EIM) is a set of business [blank_start]processes[blank_end], [blank_start]policies[blank_end] and software [blank_start]solutions[blank_end] used to [blank_start]manage data[blank_end] (internal, external, structured and unstructured). Organisations utilise EIM to ensure a whole-of-organisation (enterprise) view is taken. EIM seeks to increase [blank_start]efficiency, security and effectiveness[blank_end] of data use and enhance transparency. It permits data integration across an enterprise, providing users with a unified view of the enterprise. It also promotes collaboration across the organisation, [blank_start]improved data quality[blank_end] and enables the organisation to respond to market demands.

    1. Aprocesses
    2. Bpolicies
    3. Csolutions
    4. Dmanage data
    5. Eefficiency, security and effectiveness
    6. Fimproved data quality
24. 24The seven building blocks of EIM are:
    • [blank_start]Vision[blank_end]
    • [blank_start]Strategy[blank_end]
    • [blank_start]Metrics[blank_end]
    • [blank_start]Governance[blank_end]
    • [blank_start]People[blank_end]
    • [blank_start]Process[blank_end]
    • [blank_start]Infrastructure[blank_end]

    1. AVision
    2. BStrategy
    3. CMetrics
    4. DGovernance
    5. EPeople
    6. FProcess
    7. GInfrastructure
25. 25Seven building blocks of enterprise information management
    - vision -> data + analytics leaders must understand the vision of the organisation including strategic [blank_start]initiatives[blank_end], [blank_start]objectives[blank_end] and [blank_start]threats[blank_end]
    - Strategy -> a [blank_start]long[blank_end]-term plan for realising the EIM vision that requires commitment, a roadmap and structures ([blank_start]systems[blank_end], standards, [blank_start]policies[blank_end], [blank_start]procedures[blank_end] and organisational structures)
    - Metrics -> define the outcomes to be met to demonstrate the value of their initiative (e.g. [blank_start]product development[blank_end], [blank_start]speed of getting product to market[blank_end], [blank_start]customer satisfaction[blank_end]…)
    - Governance -> [blank_start]responsibility[blank_end], authority and accountability for setting decision rights, policies and goals for data and analytics for programs
    - People -> getting them to [blank_start]follow[blank_end] good policies and procedures
    - process -> [blank_start]define[blank_end] and [blank_start]document[blank_end] the information processes or life cycles for information or so much data will accumulate that it will become unmanageable
    - infrastructure -> create [blank_start]connections[blank_end] among disparate technologies, opportunities and sources without wholesale replacement e.g. build connections between mobile, cloud and new external sources of information

    1. Ainitiatives
    2. Bobjectives
    3. Cthreats
    4. Dlong
    5. Esystems
    6. Fpolicies
    7. Gprocedures
    8. Hproduct development
    9. Icustomer satisfaction
    10. Jspeed of getting product to market
    11. Kresponsibility
    12. Lfollow
    13. Mdocument
    14. Ndefine
    15. Oconnections
26. 26Why is managing and sharing data important?
    [blank_start]Managing[blank_end] and [blank_start]sharing[blank_end] data is important to all businesses as it allows data to be [blank_start]analysed[blank_end] across different [blank_start]management[blank_end] levels. Being able to [blank_start]share[blank_end] that information and [blank_start]knowledge[blank_end] across the business is a very [blank_start]valuable[blank_end] resource to have.

    1. AManaging
    2. Bsharing
    3. Canalysed
    4. Dmanagement
    5. Eshare
    6. Fknowledge
    7. Gvaluable
27. 27- Data standards are [blank_start]pre-defined[blank_end], [blank_start]documented[blank_end] and agreed ways to collect, classify, [blank_start]manage[blank_end] and store data
    - data standards significantly speed up the tasks of [blank_start]sharing and joining[blank_end] data
    - data standards significantly contribute to rules and [blank_start]automation[blank_end]
    - are [blank_start]mandatory[blank_end] in government/compliance reporting
    - include classification, lists, how data should be [blank_start]structured[blank_end] (e.g. street addresses, lists of languages, storage of client information)

    1. Apre-defined
    2. Bdocumented
    3. Cmanage
    4. Dsharing and joining
    5. Eautomation
    6. Fmandatory
    7. Gstructured
28. 28List the 5 data standard types: [blank_start]international[blank_end], [blank_start]nation[blank_end], [blank_start]organisation[blank_end], [blank_start]industry[blank_end], [blank_start]others[blank_end]

    1. Ainternational
    2. Bnational
    3. Corganisation
    4. Dindustry
    5. Eothers
29. 29What are some considerations concerning data quality?
    - [blank_start]missing values[blank_end]
    - [blank_start]how data was collected[blank_end]
    o [blank_start]definitions used/assumed[blank_end]
    o [blank_start]effort vs benefit of collecting data[blank_end]
    o [blank_start]reliance on others to fix[blank_end]
    

    1. Amissing values
    2. Bhow data was collected
    3. Cdefinitions used/assumed
    4. Deffort vs benefit of collecting data
    5. Ereliance on others to fix
30. 30What are the benefits to data quality? Data quality is part of an organisation’s [blank_start]risk management[blank_end] framework since poor [blank_start]quality[blank_end] = poor [blank_start]decisions[blank_end]. Data quality gives a [blank_start]competitive advantage[blank_end] by making data [blank_start]reliable[blank_end].
    

    1. Arisk management
    2. Bquality
    3. Cdecisions
    4. Dcompetitive advantage
    5. Ereliable
31. 31Meta data can embody 3 purposes:
    1. structural = describes the [blank_start]containers of the data[blank_end]
    2. descriptive = describes [blank_start]data content[blank_end]
    3. administrative = describes [blank_start]data management[blank_end]
    

    1. Acontainers of the data
    2. Bdata content
    3. Cdata management
32. 32What are the 3 purposes metadata can embody?
    1. [blank_start]structural[blank_end]
    2. [blank_start]descriptive[blank_end]
    3. [blank_start]administrative[blank_end]

    1. Astructural
    2. Bdescriptive
    3. Cadministrative
33. 33Advantages of metadata:
    o a body of [blank_start]knowledge[blank_end]
    o a road map of the [blank_start]organisation[blank_end] already worked out
    o pre-prepared agreement on what important and definitions of data items
    o enables you to easily find [blank_start]data[blank_end] across the enterprise
    - metadata explains data [blank_start]type[blank_end], data [blank_start]structure[blank_end] and data [blank_start]source[blank_end]
    - helps to align to a common definition of what the data [blank_start]is[blank_end], its [blank_start]limitations[blank_end] and [blank_start]benefits[blank_end] even before BI commences
    

    1. Aknowledge
    2. Borganisation
    3. Cdata
    4. Dtype
    5. Estructure
    6. Fsource
    7. Gis
    8. Hlimitations
    9. Ibenefits
34. 34What is metadata? It is [blank_start]data about data[blank_end], describes [blank_start]how and when and by whom[blank_end] a [blank_start]particular set of data[blank_end] was [blank_start]collected[blank_end] and how the data is [blank_start]formatted[blank_end]

    1. Adata about data
    2. Bhow and when and by whom
    3. Cparticular set of data
    4. Dcollected
    5. Eformatted
35. 35What does MDM do?
    - Master data management (MDM) enables organizations to [blank_start]create and use[blank_end] a single version of the truth
    - MDM is a comprehensive method to [blank_start]identify, define and manage[blank_end] an organisation’s critical master data
    - MDM incorporates [blank_start]data cleansing[blank_end], [blank_start]data transformation (preparing)[blank_end] and [blank_start]data integration[blank_end] (joining) practices
    - MDM results in creating and maintaining [blank_start]one single master reference[blank_end] source for all critical business data, leading to [blank_start]fewer errors[blank_end] and less duplicated in business processes
    - e.g. chipotle went from hundreds of Excel report with replicated and conflicting data to a single dashboard using MDM

    1. Acreate and use
    2. Bidentify, define and manage
    3. Cdata cleansing
    4. Ddata transformation (preparing)
    5. Edata integration
    6. Fone single master reference
    7. Gfewer errors
36. 36What does a BI project involve? A BI project involves [blank_start]planning[blank_end], [blank_start]analysing[blank_end], [blank_start]developing[blank_end] and [blank_start]implementing[blank_end] BI activities/capabilities/tools to produce deliverables/[blank_start]insights[blank_end] for the benefit of the business and its [blank_start]stakeholders[blank_end]

    1. Aplanning
    2. Banalysing
    3. Cdeveloping
    4. Dimplementing
    5. Einsights
    6. Fstakeholders
37. 37What is a project?
    A project is a planned and managed set of activities to produce deliverables for an agreed business benefit for one or more stakeholders that:
    - [blank_start]is allocated resource[blank_end] e.g. people, funds, equipment
    - has [blank_start]risks and constraints[blank_end]
    - contributes to an [blank_start]overall organisational strategy[blank_end]
    - builds [blank_start]capability and knowledge[blank_end] within the organisation
    - has known [blank_start]roles and responsibilities[blank_end]

    1. Ais allocated resource
    2. Brisks and constraints
    3. Coverall organisational strategy
    4. Dcapability and knowledge
    5. Eroles and responsibilities
38. 38The BI Project Life Cycle depicts various stages involved in the lifespan of the project, from start to close/maintenance.
    The various stages include looking at:
    • What are the [blank_start]business priorities[blank_end] and [blank_start]expected benefits[blank_end]?
    • [blank_start]Identifying and engaging[blank_end] project stakeholders
    • Which [blank_start]BI/analytical tools will be delivered[blank_end]?
    • [blank_start]Who[blank_end] will build the BI?
    • What are the [blank_start]costs[blank_end]?
    • Which [blank_start]methodologies[blank_end] are best?
    • How will the BI be [blank_start]maintained and governed[blank_end] after the project?

    1. Abusiness priorities
    2. Bexpected benefits
    3. CIdentifying and engaging
    4. DBI/analytical tools will be delivered
    5. EWho
    6. Fcosts
    7. Gmethodologies
    8. Hmaintained and governed
39. 39Some BI projects/initiatives may fail as a result of 9 Fatal Flaws and these are:
    • #1 [blank_start]If you build it they will come[blank_end]
    • #2 [blank_start]Organisation dependency on Excel[blank_end]
    • #3 [blank_start]Data quality issues[blank_end]
    • #4 T[blank_start]here are too many options[blank_end]
    • #5 Let’s just use our [blank_start]current IT partner to do this[blank_end]
    • #6 [blank_start]Once it’s finished, it’s perfect![blank_end]
    • #7 [blank_start]Just give me a dashboard. Now![blank_end]
    • #8 Why [blank_start]is my profit figure different from yours[blank_end]?
    • #9 [blank_start]Lack of strategy[blank_end]

    1. AIf you build it they will come
    2. BOrganisation dependency on Excel
    3. CData quality issues
    4. Dhere are too many options
    5. Ecurrent IT partner to do this
    6. FOnce it’s finished, it’s perfect!
    7. GJust give me a dashboard. Now!
    8. His my profit figure different from yours
    9. ILack of strategy
40. 40There are two main methodologies that can be used when implementing a BI Project - [blank_start]Waterfall[blank_end] and [blank_start]Agile[blank_end] models.
    [blank_start]Waterfall[blank_end] does each stage of the development process once and in sequence and it is often not possible to move forward until the previous stage has been completed.
    In comparison, [blank_start]Agile[blank_end] also does each stage of the development process, but it is repeated as a series of iterations.

    1. AWaterfall
    2. BAgile
    3. CWaterfall
    4. DAgile
41. 41Waterfall model advantages
    - [blank_start]easy to manage[blank_end], each phase has [blank_start]specific deliverables[blank_end] and a [blank_start]review process[blank_end]
    - works well projects where [blank_start]requirements[blank_end] are easily understandable from the beginning
    - [blank_start]process and results are well documented[blank_end] in advance
    - more familiar for [blank_start]larger[blank_end] BI teams who each have a set role
    - easier to control [blank_start]costs[blank_end] (if stick to scope and plan)
    Waterfall model disadvantages
    - stakeholder has to [blank_start]wait[blank_end] until the end to see any deliverables
    - stakeholder can be disappointed at project completion as the work was based on the initial documentation. The result [blank_start]may not meet the customers’ expectations[blank_end].
    - very difficult to move back to [blank_start]make changes[blank_end] in the previous phases
    - the testing process starts once [blank_start]development[blank_end] is over, there is a high chance of bugs found later in development when they are expensive to fix

    1. Aeasy to manage
    2. Bspecific deliverables
    3. Creview process
    4. Drequirements
    5. Eprocess and results are well documented
    6. Flarger
    7. Gcosts
    8. Hwait
    9. Imay not meet the customers’ expectations
    10. Jmake changes
    11. Kdevelopment
42. 42Agile model advantages
    - working components are delivered earlier creating greater stakeholder [blank_start]interest[blank_end] and [blank_start]support[blank_end]
    - the client is continuously [blank_start]involved[blank_end] during every stage
    - increases the chance development [blank_start]quality[blank_end] is maintained
    - the process is completed based on [blank_start]incremental[blank_end] progress
    - the client and team know exactly what is complete/what is not reducing the [blank_start]risk[blank_end] in the development process
    - [blank_start]problems and issues identified[blank_end] and dealt with earlier on
    Agile method disadvantages
    - can be [blank_start]complex[blank_end] for small development projects
    - it requires an expert skill to [blank_start]manage[blank_end]
    - the project can go [blank_start]off track[blank_end] if authority around scope not decided early on and reinforced through the project
    - can be [blank_start]costlier[blank_end] to implement!

    1. Ainterest
    2. Bsupport
    3. Cinvolved
    4. Dquality
    5. Eincremental
    6. Frisk
    7. Gproblems and issues identified
    8. Hcomplex
    9. Imanage
    10. Joff track
    11. Kcostlier
43. 43Linking corporate and BI strategy
    DISCOVERY (what/why) + RESPONSE + MONITORING
    
    Organizations perform DISCOVERY OF THE WHAT + WHY
    - find out more about business [blank_start]problems[blank_end] and [blank_start]opportunities[blank_end] + what is [blank_start]causing them[blank_end] and [blank_start]why[blank_end]? e.g. business problem/opportunity a Woolworths manager would have with lettuces?
    Then they give their [blank_start]RESPONSE[blank_end]
    - the [blank_start]action[blank_end] taken to change or optimise business [blank_start]processes[blank_end] e.g. hire more staff who will perform well and stay, change lettuce suppliers
    And check their response by [blank_start]MONITRING[blank_end]
    - [blank_start]is the implemented change working[blank_end]?
    If it doesn’t work then do more [blank_start]DISOCVERY[blank_end] to find out what/why…

    1. Aproblems
    2. Bopportunities
    3. Ccausing them
    4. Dwhy
    5. ERESPONSE
    6. Faction
    7. Gprocesses
    8. HMONITRING
    9. Iis the implemented change working
    10. JDISOCVERY
44. 44What are performance indicators? PIs are [blank_start]measurable[blank_end] [blank_start]values[blank_end] that help [blank_start]track[blank_end] whether business [blank_start]objectives[blank_end]/targets are met, while Metrics track specific business processes.

    1. Ameasurable
    2. Bvalues
    3. Ctrack
    4. Dobjectives
45. 45At minimum a well-structured PI has the following attributes:
    1. a [blank_start]metrics[blank_end] = what is it that we are [blank_start]measuring[blank_end]
    2. a [blank_start]baseline[blank_end] = what is the current [blank_start]value[blank_end] of this measure
    3. a [blank_start]direction[blank_end] = are we [blank_start]reducing[blank_end] (breakages, time to market) or [blank_start]increasing[blank_end] (gross margins, capacity)
    4. a [blank_start]target[blank_end] = what is the desired value (can be expressed in a number of ways e.g. % increase)
    5. a [blank_start]timeframe[blank_end] = how [blank_start]long[blank_end] will it be before we reach the target

    1. Ametrics
    2. Bbaseline
    3. Cdirection
    4. Dtarget
    5. Etimeframe
    6. Fmeasuring
    7. Gvalue
    8. Hreducing
    9. Iincreasing
    10. Jlong
46. 46What is business performance management? It is the business [blank_start]processes[blank_end], [blank_start]methodologies[blank_end], [blank_start]metrics[blank_end] and [blank_start]technologies[blank_end] used by enterprises to [blank_start]measures[blank_end], [blank_start]monitor[blank_end] and [blank_start]manage[blank_end] business performance

    1. Aprocesses
    2. Bmethodologies
    3. Cmetrics
    4. Dtechnologies
    5. Emeasures
    6. Fmonitor
    7. Gmanage
47. 47Optimum performance is achieved by the steps:
    1. [blank_start]strategies[blank_end], setting goals, priorities and objectives
    2. [blank_start]plan[blank_end]: establishing initiatives and plans to achieve those goals
    3. [blank_start]monitor/analyse[blank_end]: monitor [blank_start]performance[blank_end] against [blank_start]goals and objectives[blank_end]
    4. [blank_start]act/adjust[blank_end]: taking corrective action

    1. Astrategies
    2. Bplan
    3. Cmonitor/analyse
    4. Dperformance
    5. Egoals and objectives
    6. Fact/adjust
48. 48Ethics is all about doing the [blank_start]right[blank_end] thing with data, considering the human impact from all sides and making [blank_start]decisions[blank_end] based on your [blank_start]organisational[blank_end], [blank_start]professional[blank_end] and [blank_start]personal[blank_end] values. [blank_start]Privacy[blank_end] is responsibly collecting, using and storing data about people, in line with the expectations of those people, your customers, regulations and law.

    1. Aright
    2. Bdecisions
    3. Corganisational
    4. Dprofessional
    5. Epersonal
    6. FPrivacy
49. 49Ethical Reasoning is a process that involves [blank_start]identifying[blank_end] ethical issues and making [blank_start]informed[blank_end] choices or decisions that is reasonable or fair to the benefit of those that are involved. There are four fundamental questions that should be asked when conducting Ethical Reasoning:
    1. [blank_start]Who is/are the person(s) involved[blank_end]?
    2. [blank_start]What action was taken[blank_end] or is being contemplated?
    3. What are the [blank_start]results or consequences of the act[blank_end]?
    4. Is the result [blank_start]fair or just for all stakeholders[blank_end]?

    1. Aidentifying
    2. Binformed
    3. CWho is/are the person(s) involved
    4. DWhat action was taken
    5. Eresults or consequences of the act
    6. Ffair or just for all stakeholders
50. 50There are 7 reasons why Privacy Policies are Important:
    • To build [blank_start]trust[blank_end]
    • It is the [blank_start]law[blank_end]!
    • Many [blank_start]third parties require it[blank_end]
    • To evade [blank_start]costly and expensive legal battles[blank_end]
    • To make [blank_start]more money[blank_end]
    • To [blank_start]keep what you earn[blank_end]
    • There is [blank_start]too much to risk[blank_end]

    1. Atrust
    2. Blaw
    3. Cthird parties require it
    4. Dcostly and expensive legal battles
    5. Emore money
    6. Fkeep what you earn
    7. Gtoo much to risk
51. 51Data Mining is the process of [blank_start]analysing[blank_end] [blank_start]data[blank_end] to [blank_start]identify[blank_end] useful [blank_start]patterns[blank_end] and [blank_start]insights[blank_end]. Data Mining software is used to [blank_start]analyse[blank_end] information from [blank_start]multiple[blank_end] sources to find [blank_start]trends[blank_end] and model business behaviour. Finding [blank_start]anomalies[blank_end], patterns and [blank_start]correlations[blank_end] to [blank_start]predict outcomes[blank_end] makes data more [blank_start]useable[blank_end] and is a subset of Data Science.

    1. Aanalysing
    2. Bdata
    3. Cidentify
    4. Dpatterns
    5. Einsights
    6. Fanalyse
    7. Gmultiple
    8. Htrends
    9. Ianomalies
    10. Jcorrelations
    11. Kpredict outcomes
    12. Luseable
52. 52Predictive data mining supports predictive analytics by using [blank_start]current[blank_end] and [blank_start]past[blank_end] data to make [blank_start]future[blank_end] [blank_start]predictions[blank_end]. It is the process of going through system [blank_start]databases[blank_end] and finding relevant [blank_start]data[blank_end] to [blank_start]analyse[blank_end] for the purpose of prediction.

    1. Acurrent
    2. Bpast
    3. Cfuture
    4. Dpredictions
    5. Edatabases
    6. Fdata
    7. Ganalyse
53. 53An [blank_start]ETL[blank_end] involves using [blank_start]algorithms[blank_end] (code) that [blank_start]combines[blank_end], [blank_start]cleans[blank_end] and [blank_start]moves[blank_end] data from various [blank_start]sources[blank_end] into a central data repository called a [blank_start]Data Warehouse[blank_end]. A Data Warehouse is the electronic [blank_start]storage[blank_end] of a [blank_start]large[blank_end] amount of data by a business that is designed for [blank_start]reporting[blank_end] and [blank_start]data analysis[blank_end] instead of transaction processing.

    1. AETL
    2. Balgorithms
    3. Ccombines
    4. Dcleans
    5. Emoves
    6. Fsources
    7. GData Warehouse
    8. Hstorage
    9. Ilarge
    10. Jreporting
    11. Kdata analysis
54. 54ETL + DW enables [blank_start]dashboards[blank_end], impact [blank_start]diagrams[blank_end] and [blank_start]scorecards[blank_end]. Data from different systems brought together for summary and detailed analysis

    1. Adashboards
    2. Bdiagrams
    3. Cscorecards
55. 55- Three components needed to transform a device to an IoT device:
    1. [blank_start]physical[blank_end]: products mechanical and electrical parts
    2. [blank_start]smart[blank_end]: sensors, data storage, embedded operating system to replace mechanical or electrical parts
    3. [blank_start]Connectivity[blank_end]: ports, antennae and protocols enabling wired or wireless connectivity

    1. Aphysical
    2. Bsmart
    3. CConnectivity
56. 56Name and describe two end user groups in Business Intelligence. The [blank_start]data analyst[blank_end] and the [blank_start]executive[blank_end]. The executive is interested in business intelligence as it assists with [blank_start]decision making[blank_end] and [blank_start]reducing risks[blank_end] associated with those decisions. The data analyst is an end user of Business Intelligence as they might be required to [blank_start]analyse[blank_end] and [blank_start]interpret[blank_end] the data before [blank_start]reporting[blank_end] this to the CEO.

    1. Adata analyst
    2. Bexecutive
    3. Cdecision making
    4. Dreducing risks
    5. Eanalyse
    6. Finterpret
    7. Greporting
57. 57Name and describe a typical application of association rule mining.
    To anticipate [blank_start]customer behaviour[blank_end] through [blank_start]market basket analysis[blank_end] and [blank_start]recommender systems[blank_end]. Market Basket Analysis scrutinises the [blank_start]products[blank_end] customers tend to buy together and uses the [blank_start]information[blank_end] to decide which products should be cross sold or promoted together. Recommender systems learn [blank_start]patterns[blank_end] of behaviours to [blank_start]predict[blank_end] (forecast) a person’s [blank_start]preference[blank_end] on a set of items they have not yet experienced. The main goal of recommender systems is product up-sell and/or cross-sell. For example, if you purchase a beer and you don't buy a meal then you are more likely to buy a snack.

    1. Acustomer behaviour
    2. Bmarket basket analysis
    3. Crecommender systems
    4. Dproducts
    5. Einformation
    6. Fpatterns
    7. Gpredict
    8. Hpreference
58. 58Mention and describe two advantages of data visualisation over traditional
    reporting tools.
    - data [blank_start]more readily available[blank_end], [blank_start]possibilities of what could be done[blank_end] grew significantly
    - supported by [blank_start]faster and more affordable[blank_end] [blank_start]software[blank_end], hardware and storage
    - demand for accessing data driven by business end users especially management
    - mobile and internet technologies supported data availability and being [blank_start]shareable[blank_end]
    - led to [blank_start]greater involvement[blank_end] by management and [blank_start]end users[blank_end] for [blank_start]improved[blank_end] decision making
    

    1. Amore readily available
    2. Bpossibilities of what could be done
    3. Cfaster and more affordable
    4. Dsoftware
    5. Eshareable
    6. Fgreater involvement
    7. Gend users
    8. Himproved
59. 59Describe what a data warehouse is and how it helps BI.
    A Data Warehouse is the [blank_start]electronic storage[blank_end] of a [blank_start]large[blank_end] amount of data by a business that is designed for [blank_start]reporting[blank_end] and [blank_start]data analysis[blank_end] instead of transaction processing. It helps Bi through
    -supporting regular [blank_start]updates[blank_end] and occasional updates of data
    - [blank_start]storing and promoting master data[blank_end] (e.g. students, locations), data standards (e.g. countries, industry classifications), [blank_start]metadata[blank_end] and [blank_start]data quality[blank_end]
    - supports all [blank_start]levels[blank_end] of [blank_start]management decision making[blank_end] (strategic, tactical, operational) including metrics and PIs

    1. Aelectronic storage
    2. Blarge
    3. Creporting
    4. Ddata analysis
    5. Eupdates
    6. Fstoring and promoting master data
    7. Gmetadata
    8. Hdata quality
    9. Imanagement decision making
    10. Jlevels
60. 60Why do organisations need data analytics and data mining in a competitive business
    environment? Businesses that utilize [blank_start]data mining[blank_end] are able to have a [blank_start]competitive advantage[blank_end], better understanding of their [blank_start]customers[blank_end], good understanding of business [blank_start]operations[blank_end], improved [blank_start]customer[blank_end] acquisition and create new business [blank_start]opportunities[blank_end] through finding relevant data to [blank_start]analyse[blank_end]. [blank_start]Data analytics[blank_end] will allow the business to identify specified [blank_start]goals[blank_end] with better [blank_start]choices on how and where[blank_end] to take the business which will ultimately [blank_start]increase[blank_end] [blank_start]revenue[blank_end], [blank_start]market share[blank_end] and improve overall [blank_start]profitability[blank_end].

    1. Adata mining
    2. Bcompetitive advantage
    3. Ccustomers
    4. Doperations
    5. Ecustomer
    6. Fopportunities
    7. Ganalyse
    8. HData analytics
    9. Igoals
    10. Jchoices on how and where
    11. Kincrease
    12. Lrevenue
    13. Mmarket share
    14. Nprofitability
61. 61Why has Business Intelligence become important for organisations?
    One of the main reasons why BI is so important in today’s current climate is that business environments can change [blank_start]often[blank_end] and very [blank_start]quickly[blank_end]. BI can inform us, by [blank_start]monitoring[blank_end] a variety of factors, providing [blank_start]real-time[blank_end] data/information to better and rapidly allow for informed [blank_start]decision making[blank_end]. Gathering the right data at the right time is important for any business to succeed particularly in these [blank_start]turbulent[blank_end] business [blank_start]environments[blank_end].

    1. Aoften
    2. Bquickly
    3. Cmonitoring
    4. Dreal-time
    5. Edecision making
    6. Fturbulent
    7. Genvironments
62. 62 What are the two main information system development approaches in the BI Project lifecycle? Describe each one briefly.
    There are two main methodologies that can be used when implementing a BI Project - [blank_start]Waterfall[blank_end] and [blank_start]Agile[blank_end] models.
    [blank_start]Waterfall[blank_end] does each stage of the development process [blank_start]once[blank_end] and in [blank_start]sequence[blank_end] and it is often [blank_start]not possible to move forward[blank_end] until the [blank_start]previous stage[blank_end] has been [blank_start]completed[blank_end].
    In comparison, [blank_start]Agile[blank_end] also does [blank_start]each stage of the development process[blank_end], but it is repeated as a series of [blank_start]iterations[blank_end].
    
    

    1. AWaterfall
    2. BAgile
    3. CWaterfall
    4. Donce
    5. Esequence
    6. Fnot possible to move forward
    7. Gprevious stage
    8. Hcompleted
    9. IAgile
    10. Jeach stage of the development process
    11. Kiterations
63. 63Name three advantages and disadvantages of the two main methodologies that can be used when implementing a BI Project, Waterfall and Agile models.
    Waterfall:
    1. [blank_start]easy to manage[blank_end], each phase has [blank_start]specific deliverables[blank_end] and a [blank_start]review process[blank_end]
    2. easier to [blank_start]control costs[blank_end]
    3. [blank_start]process and results[blank_end] are well documented in advance
    Agile:
    1. the client is [blank_start]continuously involved[blank_end] during every stage
    2. [blank_start]increases the chance development quality[blank_end] is maintained
    3. [blank_start]problems and issues identified[blank_end] and [blank_start]dealt with earlier on[blank_end]

    1. Aeasy to manage
    2. Bspecific deliverables
    3. Creview process
    4. Dcontrol costs
    5. Eprocess and results
    6. Fcontinuously involved
    7. Gincreases the chance development quality
    8. Hproblems and issues identified
    9. Idealt with earlier on
64. 64What is the difference between a metric and a performance indicator?
    
    A metric is a [blank_start]business activity[blank_end] that is [blank_start]measured[blank_end] while a [blank_start]performance indicator[blank_end] is a [blank_start]metric[blank_end] with a [blank_start]target[blank_end]. So PIS [blank_start]track[blank_end] whether you hit [blank_start]business objectives[blank_end]/targets while [blank_start]metrics[blank_end] track [blank_start]processes[blank_end]. For example, number of nurses working in emergency ward is a metric while a performance indicator would be increase the number of nurses working in the emergency ward by 5% within 6months.

    1. Abusiness activity
    2. Bmeasured
    3. Cperformance indicator
    4. Dmetric
    5. Etarget
    6. Ftrack
    7. Gbusiness objectives
    8. Hmetrics
    9. Iprocesses
65. 65What are four fundamental questions that should be asked when conducting Ethical Reasoning:
    1. [blank_start]Who is/are the person(s) involved?[blank_end]
    2. What action [blank_start]was taken or is being contemplated[blank_end]?
    3. What are the [blank_start]results or consequences of the act[blank_end]?
    4. Is the [blank_start]result fair or just for all stakeholders[blank_end]?

    1. AWho is/are the person(s) involved?
    2. Bwas taken or is being contemplated
    3. Cresults or consequences of the act
    4. Dresult fair or just for all stakeholders
66. 66Name and describe a type of natural language processing analysis. Give one example
    application that would use natural language processing analysis.
    
    [blank_start]Voice[blank_end] is a type of natural language processing analysis to learn about a customer's sentiment, [blank_start]opinion[blank_end] or [blank_start]emotion[blank_end]. It can be used to evaluate whether someone is more [blank_start]likely[blank_end] to buy an [blank_start]expensive[blank_end] product for example.

    1. AVoice
    2. Bopinion
    3. Cemotion
    4. Dlikely
    5. Eexpensive
67. 67What is metadata? Give one example of metadata.
    
    Metadata is data about [blank_start]data[blank_end]. It provides [blank_start]descriptions[blank_end] about data to aid the [blank_start]understanding[blank_end] of the [blank_start]source[blank_end] and relevant [blank_start]characteristics[blank_end] of data including [blank_start]changes[blank_end] made to data. It can embody 3 purposes:
    1. [blank_start]structural[blank_end] = describes the containers of the data
    2. [blank_start]descriptive[blank_end] = describes data content
    3. [blank_start]administrative[blank_end] = describes data management
    An example is [blank_start]iTunes[blank_end] uses metadata to sort our music.
    

    1. Adata
    2. Bdescriptions
    3. Cunderstanding
    4. Dsource
    5. Echaracteristics
    6. Fchanges
    7. Gstructural
    8. Hdescriptive
    9. Iadministrative
    10. JiTunes
68. 68Name two capabilities of data quality tools.
    1. it can [blank_start]fix[blank_end] each [blank_start]inconsistency or ambiguity[blank_end] manually through [blank_start]profiling or cleansing[blank_end]
    2. [blank_start]more time and cost efficient[blank_end] to use [blank_start]automated data quality tools[blank_end]

    1. Ainconsistency or ambiguity
    2. Bfix
    3. Cprofiling or cleansing
    4. Dmore time and cost efficient
    5. Eautomated data quality tools
69. 69Why is data privacy, consent and ethics more important now than ever before?
    - [blank_start]Changes in Australian privacy laws[blank_end]
    - Data used in ways [blank_start]not previously possible[blank_end] from more sources
    - [blank_start]New uses of data[blank_end]
    - [blank_start]Organisations have personal data[blank_end]

    1. AChanges in Australian privacy laws
    2. BData used in ways not previously possibl
    3. CNew uses of data
    4. DOrganisations have personal data
    5. Enot previously possible
    6. Fpreviously possible
    7. GNew uses of data
    8. HOrganisations have personal data
    9. IData used in ways not previously possibl
    10. JChanges in Australian privacy laws
    11. KOrganisations have personal data
    12. LNew uses of data
    13. MData used in ways not previously possibl
    14. NChanges in Australian privacy laws
70. 70Explain the difference between business intelligence and business analytics.
    
    [blank_start]Business Intelligence[blank_end] provides [blank_start]organisational[blank_end] capacity to make [blank_start]reports[blank_end] and [blank_start]dashboards[blank_end]
    to [blank_start]multiple[blank_end] groups of users.
    E.g. Starbucks managers could receive [blank_start]daily reports[blank_end] of customer [blank_start]loyalty[blank_end] sign-up, discounted offers [blank_start]claimed[blank_end], amounts [blank_start]spent[blank_end] by loyalty customers, etc.
    
    Whereas, [blank_start]Business Analytics[blank_end] uses specialised technical [blank_start]tools and methods[blank_end] for advanced data
    analysis and predictive modelling over the large and [blank_start]complex[blank_end] data.
    E.g. To produce the daily reports and dashboard data analysts could analyse the data using pivot tables, charts, regression analysis for prediction and other analytical tools such as [blank_start]market basket analysis[blank_end] to encourage association rule spending.

    1. ABusiness Intelligence
    2. Borganisational
    3. Creports
    4. Ddashboards
    5. Emultiple
    6. Fdaily reports
    7. Gloyalty
    8. Hclaimed
    9. Ispent
    10. JBusiness Analytics
    11. Ktools and methods
    12. Lcomplex
    13. Mmarket basket analysis
71. 71What are the stages of the Waterfall/Agile information system development approaches?
    
    1. [blank_start]Define[blank_end]
    2. [blank_start]Analyse[blank_end]
    3. [blank_start]Build[blank_end]

    1. ADefine
    2. BAnalyse
    3. CBuild
72. 72What are the two types of regression models?
    1. [blank_start]value prediction[blank_end] and uses [blank_start]linear[blank_end] regression e.g. how much revenue will be generated over a year
    2. [blank_start]classification[blank_end] models and uses [blank_start]ordinal[blank_end] regression e.g. what will be most popular

    1. Avalue prediction
    2. Blinear
    3. Cclassification
    4. Dordinal
73. 73What is the difference between logistic regression and ordinal regression?
    [blank_start]Logistic[blank_end] regression has only two answers e.g. yes/ no whereas [blank_start]ordinal[blank_end] regression has more than two answers e.g. sunny, cloudy, rainy, snow

    1. ALogistic
    2. Bordinal
74. 74What is support?
    Itemset [blank_start]popularity[blank_end] calculated as a percentage
    
    What is Confidence?
    [blank_start]Probability[blank_end] of one item being purchased when another is purchased
    
    What is lift?
    compares how [blank_start]confident[blank_end] we are in the [blank_start]association rule[blank_end] [blank_start]compared[blank_end] to how [blank_start]likely[blank_end] the item is to be purchased anyway

    1. Apopularity
    2. BProbability
    3. Cconfident
    4. Dassociation rule
    5. Ecompared
    6. Flikely
75. 75What does Lift <1 = [blank_start]low[blank_end] association between the antecedent and consequent
    What does Lift >1 = [blank_start]high[blank_end] association between the antecedent and consequent
    What does Lift = 1 = [blank_start]no[blank_end] association

    1. Alow
    2. Bhigh
    3. Cno
76. 76 State the correct order of the four steps of Business Performance Management
    1. [blank_start]Get the data[blank_end]
    2. [blank_start]Use the data[blank_end]
    3. [blank_start]Optimise organisational performance[blank_end]
    4. Drive [blank_start]changes in organisational processes[blank_end]

    1. AGet the data
    2. BUse the data
    3. COptimise organisational performance
    4. Dchanges in organisational processes
77. 77State the correct order of the four steps of the Business Intelligence Performance
    Management cycle.
    1. [blank_start]strategise[blank_end]
    2. [blank_start]plan[blank_end]
    3. [blank_start]monitor/analyse[blank_end]
    4.[blank_start]act/adjust[blank_end]

    1. Astrategise
    2. Bplan
    3. Cmonitor/analyse
    4. Dact/adjust
78. 78What is metadata? Give one example of metadata.
    Metadata is a [blank_start]description[blank_end] about data to aid in [blank_start]understanding[blank_end] the data source and any
    relevant [blank_start]characteristics[blank_end] of data.
    An example of metadata is an ABS quality statement.

    1. Adescription
    2. Bunderstanding
    3. Ccharacteristics
79. 79Name two capabilities of data quality tools.
    1. [blank_start]profiling[blank_end]
    2. [blank_start]cleansing[blank_end]

    1. Aprofiling
    2. Bcleansing
80. 80Name and explain two reasons why information management is crucial for BI.
    - [blank_start]Flexibility[blank_end], scalability - can grow and change easily (like organisational agility)
    • [blank_start]Reliability[blank_end], performance - data is available when needed, especially for time limited
    users
    • [blank_start]Security[blank_end] - only accessible to those who need it
    • Handle all types and volumes of [blank_start]data[blank_end] (pictures, social media, maps, sound, etc)
    • From all types of [blank_start]sources[blank_end]: databases, mobile apps, external data feeds, fridge
    sensors,

    1. AFlexibility
    2. BReliability
    3. CSecurity
    4. Dsources
    5. Edata

Descripción

Test sobre Business Intelligence, creado por Steph McAllister el 28/08/2021.

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Steph McAllister

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